A framework for ranking uncertain distributed database

Distribution and uncertainty are considered as the most important design issues in database applications nowadays. A lot of ranking or top-k query processing techniques are introduced to solve the problems of communication cost and centralized processing. On the other hand, many techniques are also developed for modeling and managing uncertain databases. Although these techniques were efficient, they didn't deal with distributed data uncertainty. This paper proposes a framework that deals with both data distribution and uncertainty based on ranking queries. Within the proposed framework, communication and computation-efficient algorithms are investigated for retrieving the top-k tuples from distributed sites. The main objective of these algorithms is to reduce the communication rounds utilized and amount of data transmitted while achieving efficient ranking. Experimental results show that both proposed techniques have a great impact in reducing communication cost. Both techniques are efficient but in different situations. The first one is efficient in the case of low number of sites while the other achieves better performance at higher number of sites.